For the disentanglement model, we adopt the one-layer GRU [1]} for the encoder and decoder networks with hidden size 64. For the four predictors, we leverage the 2-layer stacked MLP network with batch normalization [2]}, the activation function is tanh. As introduced, the market return prediction is a classification task while the excess return prediction is a regression task. Hence, one three class softmax layer is used for market return predictors, while one unit regression for excess return predictors. The regression loss is MSE for excess returns and the classification loss is cross entropy for market returns.
We use Adam [3]} optimizer with the learning rate \(0.001\)  for training. Dropout [4]} is used for the decoder output with value \(0.5\) , the batch size is \(5,000\) .
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